Study on Smart Contract Vulnerability Repair Based on T5 Model

The current research on addressing vulnerabilities in Ethereum smart contracts primarily focuses on manually defined templates.This method requires developers to have extensive expertise,and its effectiveness is poor when dealing with complex vulnerabilities.This paper explores vulnerability repair techniques for smart contracts at the source code level in Solidity.By introducing a machine learning approach to vulnerability repair,we designe and implement a T5 model-based smart contract vulnerability repair system to tackle the problem of depending on manual intervention.Using data crawling and data augmentation techniques,we compile a training dataset specifically for the T5 model.The T5 model for repairing smart contract vulnerabilities is trained using machine learning techniques.A test dataset is constructed through web crawling to evaluate the system’s perfor-mance from various perspectives.The system’s accuracy in contract repair,gas consumption,and introduced code volume is compared with other contract vulnerability repair tools such as TIPS,SGUARD,and Elysium.Experimental results show that our system achieves good repair outcomes and overall performance superior to other vulnerability repair tools.